Design of a Fall Detection Product Based on Arduino

Abstract

The article proposes a design scheme for a fall detection product. The design utilizes a MAX6050 three-axis gyroscope sensor and a two-axis angular velocity sensor for fall detection. At the same time, a vector machine model is established, combining the advantages of both the threshold method and machine learning to reduce the probability of misjudging falls. The hardware of this system consists of five components: a triaxial acceleration data acquisition module, a biaxial angular velocity data acquisition module, a data analysis and processing module, a GPS positioning module, and a wireless data transmission module. The triaxial acceleration data acquisition module and the biaxial angular velocity data acquisition module collect human acceleration and angular velocity data, which are further processed through a threshold algorithm and a support vector machine model to effectively distinguish between daily activities and falling behaviors, thereby reducing the probability of false fall detections. Simultaneously, the system sends the location information of the injured person via data transmission, achieving a closed loop of fall detection and rescue. Experimental results demonstrate that this design can accurately detect fall behaviors, deploy an airbag within 1 second, and promptly send location information, providing a new approach for the research on anti-fall products.